For proper navigation of an autonomous vehicle, the autonomous vehicle needs to identify various text information available in surroundings of a path being traveled by the autonomous vehicle. However, performing a relative text identification among the various text information, and deriving a meaning from the available text information may be a challenging task for the autonomous vehicles. For example, during navigation, the autonomous vehicles may encounter numerous traffic signboards and other information boards, which contain various text information. In such instances, the autonomous vehicle may find it difficult to differentiate between the traffic signboards and the other information boards which in turn makes the relative text identification process a complex issue. Identifying and differentiating the traffic signboards from the other information boards helps in eliminating undue computational overheads in the text identification process, as the other information boards can be excluded from the text identification process.
The complexity of the text identification process may further increase, when the text information in the traffic boards or other information boards along the roadside comprise a combination of text information in multiple text formats or styles, such as vertical text format, horizontal text format or semi-circular text format. This is because, a text detection technique to be used for detecting and extracting the text information would vary during vehicle navigation, depending on formats or styles of each text information. Further, one of the major challenges lies in establishing a correct relationship between the identified text information and upcoming scenes and/or changes on the road.
Currently, one or more existing arts in the domain exhibit limited applicability in identification of orientation of the text information on the information boards. For example, the existing methods differentiate between text information that run left-to-right or top-to-bottom, or text information having same font, size and the like. However, the existing methods fail to identify a relationship among the text information that are in complex text formats or styles such as, semi-circular, circular, mixed font style, mixed font size and the like, and thereby result in perspective errors in the analysis.